1. Field of the Invention
The invention relates generally to a PRML (Partial Response Maximum Likelihood) system, and in particular to a PRML system with a branch estimator and a tunable Viterbi decoder.
2. Description of the Related Art
A PRML (Partial Response Maximum Likelihood) system is used to retrieve EFM (Eight-to-Fourteen Modulation) signals of CD (Compact Disc)/DVD (Digital Versatile Disk) more reliably, and a Viterbi decoder is usually to realize the maximum likelihood detection in the PRML system. In practice, the PRML system handles the channels that are uncertain and varying, and two conventional methods are proposed to solve this problem in the PRML system.
FIG. 1 is a block diagram showing a PRML system with an adaptive equalizer according to a first conventional method. As shown in FIG. 1, the PRML system 10 includes an adaptive equalizer 11 and a Viterbi decoder 12. The Viterbi decoder 12 has to receive constant parameters, and the adaptive equalizer 11 is used to reshape the waveform of the input signal to fit the requirement of the Viterbi decoder 12. Then, the Viterbi decoder 12 can generate the output signal. FIG. 2 is a block diagram showing a PRML system with a channel estimator according to a second conventional method. As shown in FIG. 2, the PRML system 20 includes a Viterbi decoder 21 and a channel estimator 22. The Viterbi decoder 21 receives an input signal and generates an output signal. The Viterbi decoder 21 may receive tunable parameters, and the channel estimator 22 is used to estimate and tune the desired parameters needed by the Viterbi decoder 21 according to the input signal. Then, the Viterbi decoder 21 can generate the output signal according to the tuned parameters and the input signal.
Currently, the PRML system 10 of the first method is widely used. However, the adaptive equalizer 11 thereof needs an adaptive filter (equalizer 11) to process the waveform of the input signal, thereby needing a lot of hardware and the execution speed being limited. Although an adaptive filter with pipeline architecture can increase the speed, the pipelining latency of the equalized signal may suffer the problem of system un-stability.
Another problem of the conventional PRML system is that the performance of the PRML system is reduced with the phase error of the sampling clock. In the conventional data slicing method, the sliced signal is in the binary form, which means that the channel clock has large tolerance of phase error to latch the sliced signal correctly. However, the sampling clock in the PRML system is more critical than the channel clock required in the data slicing method, because any small phase error may generate a magnitude error of the sampled value. In addition, the sampling clock in the PRML system is used to trigger an ADC (Analog-to-Digital converter), and the performance of the high speed ADC is very sensitive to the jitter of the sampling clock.
A feature of a clock recovery for the PRML system is that the sampling clock has to be synchronized with the output signal (sampled data) of ADC, rather than the input signal. That is, the phase error is estimated from the sampled data instead of the input signal. FIGS. 3 and 4 show two PRML systems with different clock recovery block diagrams, respectively. As shown in FIG. 3, the PRML system utilizes a clock recovery unit 33, which receives the output signal of the adaptive equalizer 11, to provide a sampling clock to the ADC 32. An analog signal equalizer 31 is further provided in front of the ADC 32 to receive and equalize an input signal. The PRML system as shown in FIG. 4 provides a sampling clock with fixed frequency to the ADC 32, and resample the output of the ADC 32 at the desired sample phases with a digital signal equalizer and timing interpolation unit 41.
FIG. 5 is a block diagram showing a typical adaptive channel estimator applied in the PRML system of FIG. 2. The channel estimator includes a channel 51, a FIR (Finite Impulse Response) filter 52, an adaptive weighting controller 53, and an adder 54. The channel estimator utilizes the FIR filter 52 to find out the characteristic of the channel 51, which is called as a model-directed estimator accordingly. In general, to implement the FIR filter 52 requires multipliers, which limits the operation speed. Meanwhile, the channel estimator finds out the channel model of the channel (i.e., linear channel coefficients), so an additional circuit (not shown) has to be used to convert the channel coefficients of the channel model into branch values required by the Viterbi decoder during decoding. Therefore, the adaptive channel estimator is inefficient and also needs many redundant circuits.